Pattern recognition is an aspect of the field of artificial intelligence aiming at providing perceptions to “intelligent” systems, such as robots, programmable controllers, speech recognition systems, artificial vision systems, sensorial substitution systems, and the like.
In pattern recognition, objects are classified according to some chosen criteria so as to allow these objects to be compared with each other, for example by comparing a target object with a well-known, basic object. Comparison is made by computing a distance between the base and target objects as a function of the chosen criteria. Accordingly, it is possible, for example, to quantify the similarity or dissimilarity between two objects, to remember an object and to recognize this object later on.
An object, as referred to hereinabove, is not restricted to a physical shape or a visual representation; it has to be understood that an object means any entity that may be represented by a signal.
In general, but not restrictively, the term “distance” may be construed as a mathematical function for measuring a degree of dissimilarity between two objects. For example, if the two objects are assimilated to two respective vectors, this distance may be the Euclidian norm of the difference between the two vectors. The distance could also be, for example, a probability, an error, a score, etc.
Those of ordinary skill in the art of rule-based expert systems, statistical Markovian systems or second generation neural network systems are familiar with such a concept of “distance”.
Unfortunately, pattern recognition is often an important computational burden. Furthermore, object comparison—or more generally comparison between physical entities of any type—is usually obtained by first comparing segments of the objects, which involves computationally intensive distance comparison. Additionally, object comparison is based on the premise that there is a well-defined basic object for use as a comparison base for characterizing a target object. Such basic object is not always available and techniques relying on the availability of basic objects are not well-suited for characterizing new or different objects.
Therefore, there is a need for an efficient technique for recognizing internal structures of physical entities, or objects, while reducing the amount of computation time required to provide a usable structure representation.